Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks

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1 Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks M. Hashem Pesaran University of Cambridge and USC Allan Timmermann University of California, San Diego May 2003, this version February 2004 Abstract This paper develops a theoretical framework for the analysis of smallsample properties of forecasts from general autoregressive models under structural breaks. Finite-sample results for the mean squared forecast error of one-step ahead forecasts are derived, both conditionally and unconditionally, and numerical results for different types of break specifications are presented. It is established that forecast errors are unconditionally unbiased even in the presence of breaks in the autoregressive coefficients and/or error variances so long as the unconditional mean of the process remains unchanged. Insights from the theoretical analysis are demonstrated in Monte Carlo simulations and on a range of macroeconomic time series from G7 countries. The results are used to draw practical recommendations for the choice of estimation window when forecasting from autoregressive models subject to breaks. JEL Classifications: C22, C53. Key Words: Small sample properties of forecasts, MSFE, structural breaks, autoregression, rolling window estimator. We are grateful to the editor, four referees, and seminar participants at Cass Business School (London) for helpful comments on an earlier version of the paper. We would also like to thank Mutita Akusuwan for excellent research assistance.

2 1. Introduction Autoregressive models are used extensively in forecasting throughout economics and finance and have proved so successful and difficult to outperform in practice that they are frequently used as benchmarks in forecast competitions. Due in large part to their relatively parsimonious form, autoregressive models are frequently found to produce smaller forecast errors than those associated with models that allow for more complicated nonlinear dynamics or additional predictor variables, c.f. Stock and Watson (1999) and Giacomini (2002). Despite their empirical success, there is now mounting evidence that the parameters of autoregressive (AR) models fitted to many economic time series are unstable and subject to structural breaks. For example, Stock and Watson (1996) undertake a systematic study of a wide variety of economic time series and find that the majority of these are subject to structural breaks. Alogoskoufis and Smith (1991) and Garcia and Perron (1996) are other examples of studies that document instability related to the autoregressive terms in forecasting models. Clements and Hendry (1998) view structural instability as a key determinant of forecasting performance. This suggests a need to study the behaviour of the parameter estimates of AR models as well as their forecasting performance when these models undergo breaks. Despite the interest in econometric models subject to structural breaks, little is known about the small sample properties of AR models that undergo discrete changes. In view of the widespread use of AR models in forecasting, this is clearly an important area to investigate. The presence of breaks makes the focus on small sample properties more relevant: even if the combined pre- and post-break sample is very large, the occurrence of a structural break means that the post-break sample will often be quite small so that asymptotic approximations may not be nearly as accurate as is normally the case. A key question that arises in the presence of breaks is how much data to use to estimate the parameters of the forecasting model that minimizes a loss function such as root mean squared forecast error (RMSFE). We show that the RMSFEminimizing estimation window crucially depends on the size of the break as well as its direction (i.e., does the break lead to higher or lower persistence) and which parameters it affects (i.e., the mean, variance or autoregressive slope parameters). In some situations the optimal estimation window trades off an increased bias 1

3 introduced by using pre-break data against a reduction in forecast error variance resulting from using a longer window of the data. However, in other situations the small sample bias in the autoregressive coefficients may in fact be reduced after introducing pre-break data if the size of the break is small or even when the break is large provided that it is in the right direction (e.g., when persistence declines). In the presence of parameter instability it is common to use a rolling window estimatorthatmakesuseofafixed number of the most recent data points, although the size of the rolling window is based on pragmatic considerations rather than an empirical analysis of the underlying time series process. Another possibility would be to test for breaks in the parameters and/or error variances and only use data after the most recent break, assuming a break is in fact detected. Alternatively, if no statistically significant break is found, an expanding window estimator could be used. Our theoretical analysis allows us to better understand when each of these procedures is likely to work well and why it is generally best to use pre-break data when forecasting using autoregressive models. First, breaks in the autoregressive parameters need not introduce bias in the forecasts (at least unconditionally). This tends to happen when an autoregressive coefficient declines after a break or the break only occurs in the intercept or variance parameter. Including pre-break data in such cases will tend to lead to a decline in RMSFE due to both a smaller squared bias and a reduction in the variance of the parameter estimate. Furthermore, in practice, there is likely to be a considerable error in detecting and estimating the point of the break of the autoregressive model. This leads to a worse performance of a post-break estimation procedure but also makes determination of the length of a rolling window more difficult. Several practical recommendations emerge from our analysis regarding the choice of estimation window when forecasting from autoregressive models. First, for the macroeconomic data examined here, in general it appears to be difficult in practice to outperform expanding or long rolling window estimation methods. Unlike the case with exogenous regressors, forecasts from autoregressive models can be seriously biased even if only post-break observations are used. Including pre-break data in estimation of autoregressive models can simultaneously reduce the bias and the variance of the forecast errors. In most applications where breaks are not too large, expanding window methods or rolling window procedures with relatively large window sizes are likely to perform well. This conclusion may not of course 2

4 carry over to longer data sets, e.g. high frequency financial data with thousands of observations, where estimation uncertainty can be reduced more effectively than with the relatively short macroeconomic data considered here. The main contributions of this paper are as follows. First, we present a new procedure for computing the exact small sample properties of the parameters of AR models of arbitrary order, thus extending the existing literature that has focused on the AR(1) model. Our approach allows for fixed or random starting points and considers stationary AR models as well as models with unit root dynamics. We allow for the possibility of the AR model to switch from a unit root process to a stationary one and vice versa. Such regime switches could be particularly relevant to the analysis of inflation in a number of OECD countries since the first oil price shock in early 1970 s. In addition to considering properties such as bias in the parameters, we also consider the RMSFE in finite samples. Second, we extend existing results on exact small sample properties of AR models to allow for a break in the underlying data generating process. We establish that one-step ahead forecast errors from AR models are unconditionally unbiased even in the presence of breaks in the autoregressive coefficients and in the error variances so long as the unconditional mean of the process remains unchanged. Our results also apply to models with unit roots. This extends Fuller (1996) s result obtained for AR models with fixed parameters, and generalizes a related finding due to Clements and Hendry (1999, pp.39-42). Third, we present extensive numerical results quantifying the effect of the sizes of the pre-break and post-break data windows on parameter bias and RMSFE. Fourth, we undertake an empirical analysis for a range of macroeconomic time series from the G7 countries that compares the forecasting performance of expanding window, rolling window and post-break estimators. This analysis which allows for multiple breaks at unknown times confirms that, at least for macroeconomic time series such as those considered here, it is generally best to use pre-break data in estimation of the forecasting model. The outline of the paper is as follows. Section 2 provides a brief overview of the small sample properties of the first-order autoregressive model that has been extensively studied in the extant literature. Theoretical results allowing us to characterize the small sample distribution of the parameters and forecast errors of autoregressive models are introduced in Section 3. Section 4 presents numerical results for AR models subject to breaks and Section 5 presents empirical results 3

5 for a range of macroeconomic time series. Section 6 concludes with a summary and a discussion of possible extensions to our work. 2. Small Sample Properties of Forecasts from Autoregressive Models A large literature has studied small sample properties of estimates of the parameters of autoregressive models. The majority of studies has concentrated on deriving either exact or approximate small sample results for the distribution of ˆα T and ˆβ T, the Ordinary Least Squares (OLS) estimators of α and β, inthefirst-order autoregressive (AR(1)) model y t = α + βy t 1 + σε t,t=1, 2,..., T, ε t iid(0, 1). (1) Analysis of the small sample bias of ˆβ T dates back to at least Bartlett (1946). Early studies focus on the stationary AR(1) model without an intercept (α = 0, β < 1) but have been extended to higher order models with intercepts (Sawa (1978)) and exogenous regressors (Grubb and Symons (1987), Kiviet and Phillips (1993, 2003a)). Assuming stationarity ( β < 1), ˆβ T has been shown to have an asymptotic normal distribution and its finite-sample distribution has been studied by Phillips (1977) and Evans and Savin (1981). The case with a unit root, β =1, has been studied by, inter alia, Banerjee, Dolado, Hendry and Smith (1986), Phillips (1987), Stock (1987), Abadir (1993) and Kiviet and Phillips (2003b). To a forecaster, the bias in ˆα T and ˆβ T is of direct interest only to the extent that it might adversely influence the forecasting performance. Ullah (2003) provides an extensive discussion and survey of the properties of forecasts from the AR(1) model. Box and Jenkins (1970) characterized the asymptotic mean squared forecast error (MSFE) for a stationary first-order autoregressive process considering both the single-period and multi-period horizon. Assuming a stationary process, Copas (1966) used Monte Carlo methods to study the MSFE of least-squares and maximum likelihood estimators under Gaussian innovations. In practice, the conditional forecast error is of more interest than the unconditional error since the data needed to compute conditional forecasts is always available. A comprehensive asymptotic analysis for the stationary AR(p) model is provided in Fuller and Hasza (1981) and Fuller (1996). Using Theorem in 4

6 Fuller (1996) it is easily seen that, conditional on y T, MSFE(ŷ T +1 y T ) = E (y T +1 ŷ T +1 ) 2 y T = σ 2 (1 + 1 T )+1 β2 T This yields the more familiar unconditional result µ y T α 2 + O(T 3/2 ). 1 β MSFE(ŷ T +1 )=E (y T +1 ŷ T +1 ) 2 = σ 2 (1 + 2 T )+O(T 3/2 ). Generalizations to AR(p) and multi-step forecasts are also provided in Fuller (1996, pp ), where it is established that the forecast error, y T +1 ŷ T +1, is unbiased in small samples assuming ε t has a symmetric distribution and E ( ŷ T +1 ) <. This is particularly noteworthy considering the often large small sample bias associated with estimates of the autoregressive parameters. 3. AR(p) Model in the Presence of Structural Breaks In parallel with the work on the small sample properties of estimates of autoregressive models, important progress has been made in testing for and estimating both the time and the size of breakpoints, as witnessed by the recent work of Andrews (1993), Andrews and Ploberger (1996), Bai and Perron (1998, 2003), Banerjee, Lumsdaine and Stock (1992), Chu, Stinchcombe and White (1996), Chong (2001), Elliott and Muller (2002), Hansen (1992), Inclan and Tiao (1994) and Ploberger, Kramer and Kontrus (1989). Building on this work we consider the small sample problem of estimation and forecasting with AR(p) models in the presence of structural breaks. For this purpose, we consider the following AR(p) model defined over the period t =1, 2,..., T ; and assumed to have been subject to a single structural break at time T 1 : y t = ( α 1 + β 11 y t 1 + β 12 y t β 1p y t p + σ 1 ε t,,for t T 1, α 2 + β 21 y t 1 + β 22 y t β 2p y t p + σ 2 ε t,,for t>t 1,. (2) As before ε t iid(0, 1) for all t. For the analysis of the unit root case it is also convenient to consider the following parameterization of the intercept terms, α i : α i = µ i (1 β i ), i =1, 2, (3) 5

7 where β i = P p j=1 β ij, = τ 0 pβ i, β i =(β i1, β i2,..., β ip ) 0 and τ p is a p 1 unit vector. Note that (1 β i )alsorepresentsthecoefficient of y t 1 in the error correction representation of (2). This specification is quite general and allows for intercept and slope shifts, as well as a change in error variances immediately after t = T 1. It is also possible for the y t process to contain a unit root (or be integrated of order 1) in one or both of the regimes. The integration property of y t under the two regimes is governed by whether β i =1orβ i < 1. More specifically, we shall assume that the roots of px λ j β ij 1 = 0, for i =1, 2, (4) j=1 lie on or outside the unit circle. 1 As µ i is allowed to vary freely, the intercepts α i = µ i (1 β i ) are unrestricted when the underlying AR processes are stationary. However, to avoid the possibility of generating linear trends in the y t process, the intercepts are restricted (α i = 0) in the presence of unit roots. In the stationary case µ i represents the unconditional mean of y t in regime i. In the unit root case µ i is not identified and we have E( y t )=0. Analysis of forecast errors from AR models subject to structural change have been recently addressed by Clements and Hendry (1998,1999). However, these authors either abstract from the problem of parameter uncertainty, or only allow for it assuming that the parameters of the AR model remain unchanged during the estimation period. Consider first the analysis provided in Clements and Hendry (1998, pp ), where it is assumed that parameters are known and the break takes place immediately prior to the forecasting period. In this case the one-step ahead forecast error is given by y T +1 ỹ T +1 = µ 2 (1 β 2) µ 1 (1 β 1)+x 0 T (β 2 β 1 )+σ 2 ε T +1, where x T =(y T,y T 1,...,y T p+1 ) 0,(µ 1, β 1 ) are the parameters prior to the forecast period, and (µ 2, β 2 ) are the parameters during the forecast period, here T +1. Following Clement and Hendry and noting that β i = τ 0 pβ i,itiseasilyverified that y T +1 ỹ T +1 =(µ 2 µ 1 )(1 β 2)+(β 2 β 1 ) 0 (x T µ 1 τ p )+σ 2 ε T +1, 1 Our analysis can also allow for the possibility of y t beingintegratedofordertwoinoneor both of the two regimes. But in this paper we shall only consider the unit root case explicitly. 6

8 and E (y T +1 ỹ T +1 )=(µ 2 µ 1 )(1 β 2)+(β 2 β 1 ) 0 E (x T µ 1 τ p ). Inthecasewherey t is stationary we have E (x T µ 1 τ p ) = 0, and E (y T +1 ỹ T +1 )=(µ 2 µ 1 )(1 β 2), which does not depend on the size of the break in the slope coefficients, β 2 β 1,and will be zero when µ 2 = µ 1. This is an interesting theoretical result but its relevance is limited in practice where estimates of (µ 1, β 1 ) based on past observations need to be used. One of the contributions of this paper might be viewed as identifying the circumstances under which the above result is likely to hold in the presence of estimation uncertainty. In a related contribution Clements and Hendry (1999, pp ) consider the effect of estimation uncertainty on the forecast error decomposition using a first-order vector autoregressive model, and conclude estimation uncertainty to be relatively unimportant. However, their analysis assumes that the estimation is carried out immediately prior to the break, based on a correctly specified model which is not subject to any breaks. The assumption that parameters have been stable prior to forecasting is clearly restrictive, and it is therefore important that a more general framework is considered where the effect of estimation uncertainty can be analysed even in the presence of multiple breaks in the parameters (slope coefficients as well as error variances) over the estimation period. In this paper we provide such a framework in the case of AR(p) models subject to a single break point over the estimation period. But, it should become clear that the analysis readily extends to two or more break points. 2 In particular, our interest in this paper lies in the point (or probability) forecast of y T +1 conditional on Ω T = {y 1,y 2,..., y T } in the context of the break point specification (2). In the case where the post-break window size, v 2 = T T 1 is sufficiently large (v 2 ), the structural break is relatively unimportant and the forecast of y T +1 can be based exclusively on the post-break observations. However, when v 2 is small it might be worthwhile to base the forecasting model on pre-break 2 Explicitly allowing for breaks and parameter uncertainty prior to forecasting also raises the issueofthechoiceofobservationwindowdiscussedinrelatedpapersinpesaranandtimmermann (2002, 2003). 7

9 as well as post-break observations. The number of pre-break observations, which we denote by v 1, then becomes a choice parameter. In what follows we assume T 1 is known but consider forecasting y T +1 using the past T m + p +1observations, m p being the starting point of the estimation window, y T (m p) =(y m p,y m p+1,..., y T1,y T , y T ) 0, (5) with the p observations y m p,y m p+1,..., y m 1 treated as given initial values. 3 The length of the pre-break window is then given by v 1 = T 1 m +1, and the number oftimeperiodsusedinestimationisthereforev = v 1 +v 2 = T m+1. To simplify the notations we shall consider values of v 1 p, orm T 1 p 1. The point forecast of y T +1 conditional on y T (m p) isgivenby ŷ T +1 (m) =ˆα T (m)+x 0 T ˆβ T (m), where x T =(y T,y T 1,..., y T p+1 ) 0, ˆβT (m) =³ˆβ1T (m), ˆβ 2T (m),..., ˆβ pt (m) 0, τ v is a v 1 vector of ones, M τ = I ν τ v (τ 0 vτ v ) 1 τ 0 v,and X T (m) =(y T 1 (m 1), y T 2 (m 2),..., y T p (m p)), so that ˆβ T (m) =[X 0 T (m) M τx T (m)] 1 X 0 T (m) M τy T (m), (6) ˆα T (m) = τ 0 vy T (m) τ 0 vx T (m) ˆβ T (m), (7) v The one-step ahead forecast error is e T +1 (m) =y T +1 ŷ T +1 (m) =σ 2 ε T +1 ξ T (m), (8) where ξ T (m) =[ˆα T (m) α 2 ]+x 0 T ³ˆβT (m) β 2. (9) β 2 =(β 21, β 22,..., β 2p ) 0 and α 2 = µ 2 1 τ 0 p β 2. We consider both the unconditional and conditional mean squared forecast error given by E ε e 2 T +1 (m) and E ε e 2 T +1 (m) Ω T, respectively, where the expectations operator Eε ( ) isdefined with respect to the distribution of the innovations ε t. TotheseehowtheMSFE 3 Throughout the paper we shall use the notation y T (k) =(y k,..., y T ) 0. 8

10 depends on the starting point of the estimation window, m, notethatε T +1 and ξ T (m) are independently distributed and E ε e 2 T +1 (m) Ω T = σ E ε ξ 2 T (m) Ω T. (10) To carry out the necessary computations, an explicit expression for ξ T (m) interms of the ε 0 ts is required. This is complicated and depends on the state of the process just before the first observation is used for estimation. For a given choice of m>pand a finite sample size T, the joint distribution of ˆβ T (m) andˆα T (m) depends on the distribution of the initial values y m 1 (m p)= (y m p,y m p+1,..., y m 1 ) 0. (11) We distinguish between the two important cases where the pre-break process is stationary and when it contains a unit root Pre-Break Process is Stationary In the case where the pre-break regime is stationary and has been in operation for sufficiently long time, the distribution of y m 1 (m p) does not depend on m and is given by y m 1 (m p) N(µ 1 τ p, σ 2 1V p ), (12) where V p is defined in terms of the pre-break parameters. For example, for p =1, V 1 =1/(1 β 2 11 ), and for p =2 Ã! 1 V 2 = (1 + β 12 ) 1 β (1 β 12 ) 2 12 β 11. β 2 11 β 11 1 β Pre-Break Process is I(1) If the pre-break process contains a unit root, the covariance of y m 1 (m p) isno longer given by σ 2 1V p and in general depends on m. Under a pre-break unit root, β 1 = 1 and the pre-break process is given by p 1 X y t = δ 1j y t j + σ 1 ε t, for t T 1, (13) j=1 where δ 1j = P p `=j+1 β 1`. The distribution of initial values can now be specified in terms of the stationary distribution of the first differences, ( y 2, y 3,..., y p ), and 9

11 an assumption concerning the first observation in the sample, y 1. In what follows we assume that y 1 is given by y 1 = µ 1 + ωε 1, (14) where ω will be treated as a free parameter, and ε 1 N(0, 1). Using (13) and (14) it is now possible to derive the distribution of the initial values, y m 1 (m p) =(y m p,y m p+1,..., y m 1 ) 0,notingthat y m i = y 1 + y y m i, for i =1, 2,..., p. In the AR(1) case we have and in conjunction with (14) we have y t = σ 1 ε t, for t =2, 3,..., T 1, y m 1 = y 1 + y y m 1 and hence y m 1 N (µ 1, V 1,m ), where = µ 1 + ωε 1 + σ 1 (ε 2 + ε ε m 1 ), V 1,m = ω 2 +(m 2)σ 2 1. (15) FortheAR(2)specification we have y m 1 (m 2) = (y m 2,y m 1 ) 0 N (µ 1 τ 2, V 2,m ), where V 2,m is derived in Appendix A OLS Estimates Using(12)and(2)fort = m, m +1,..., T, in matrix notations we have By T (m p) =d + D ε, (16) where D = σ 1 ψ p I ν1 0, d =µ 1 τ p (1 β 1)τ v1, (17) 0 0 (σ 2 /σ 1 ) I ν2 I p 0 0 B = B 21 B B 32 B (µ 2 /µ 1 )(1 β 2)τ v2. (18)

12 The sub-matrices, B ij, depend only on the slope coefficients, β 1 and β 2 and are definedinappendixb.i ν1 and I ν2 are identity matrices of order ν 1 and ν 2, respectively and ε =(ε m p, ε m p+1,..., ε T ) 0 N(0, I ν+p ). The form of ψ p depends on whether the pre-break process is stationary or contains a unit root. Under the former ψ p is a lower triangular Cholesky factor of V p,namelyv p = ψ p ψ 0 p, where V p is the covariance matrix of y m 1 (m p). Appropriate expressions for V p inthecaseofp = 1 and 2 are already provided in Section When the pre-break process has a unit root, ψ p is given by the lower triangular Cholesky factor of V p,m, which is given by (15) above for p =1andin Appendix A by (38) for p =2. Using (40) derived in Appendix B, in general we have y T i (m i) =G i (c + Hε), for i =0, 1,..., p, (19) where G i are v (v + p) selection matrices defined by G i = (0 v p i.i ν.0 v i ), H = B 1 D,andc = B 1 d. In particular, y T (m) =G 0 (c + Hε), and X T (m) = h i G 1 (c + Hε), G 2 (c + Hε),..., G p (c + Hε). Therefore, in general the (i, j) element of the product moment matrix, X 0 T (m) M τ X T (m), is given by (c + Hε) 0 G 0 im τ G j (c + Hε), for i, j =1, 2,..., p, andthej th element of X 0 T (m) M τ y T (m) isgivenby(c + Hε) 0 G 0 jm τ G 0 (c + Hε), for j =1, 2,..., p. Hence, ˆβT (m) = ³ˆβ1T (m), ˆβ 0, 2T (m),...,ˆβ pt (m) is a non-linear function of the quadratic forms (c + Hε) 0 G 0 im τ G j (c + Hε), for i =1, 2,...p, and j =0, 1,..., p, with known matrices H, G i, c, andε N(0, I ν+p ). Similarly, using (7) we have ˆα T (m) =v 1 τ 0 v G 0(c + Hε) v 1 τ 0 v In the AR(1) case these results simplify to px G i (c + Hε)ˆβ it (m). (20) i=1 ˆβ T (m) = (c + Hε)0 G 0 1M τ G 0 (c + Hε) (c + Hε) 0 G 0 1 M τg 1 (c + Hε), (21) and ˆα T (m) =v 1 τ 0 vg 0 (c + Hε) v 1 τ 0 vg 1 (c + Hε)ˆβ T (m). (22) 11

13 Using the above results in (6) it is now easily seen that in general ˆβ T (m) depends on the ratios, µ 1 /σ 1, σ 1 /σ 2 and µ 1 /µ 2 (or µ 2 /µ 1 ), whilst ˆα T (m) depends on all the four coefficients, µ 1,µ 2, σ 1,andσ 2, individually. Two cases of special interest arise when there is no mean shift in the model, and when the post-break process contains a unit root. In both cases, as shown in Appendix B, G i c =κτ v where κ = µ when there is no mean shift (i.e. µ 1 = µ 2 = µ), and κ = µ 1 if there is a mean shift but β 2 = 1. Under either of these two special cases we have M τg i c = 0, for all i, and ˆβ T (m) will be a function of the quadratic terms, ε 0 H 0 G 0 im τ G j Hε, which depend only on the ratio of the error variances, σ 1 /σ 2. These results also establish the following proposition: Proposition 1 Under µ 1 = µ 2 or if β 1 < 1 and β 2 =1, ˆβ T (m) defined by (6) does not depend on the scale of the error variances (σ 2 1, σ 2 2) or the unconditional means, µ 1, µ 2, and is an even function of ε. This proposition plays a key role in the analysis of prediction errors below. It is also worth noting that ˆβ T (m) will continue to be an even function of the errors in the more general case where the slope coefficients and/or the error variances are subject to multiple breaks, so long as the mean of the process remains unchanged. This proposition does not, however, extend to the OLS estimate of the intercept, ˆα T (m). To see this, using (20) and noting that under µ 1 = µ 2,orifβ 2 =1, G i c =µ 1 τ v we have ³ ˆα T (m) =µ 1 1 ˆβ (m) µ τ 0 T + v G 0 Hε px µ τ 0 v G i Hε ˆβ v v it (m), (23) where ˆβ T (m) =P p i=1 ˆβ it (m) =τ 0 pˆβ T (m). It is clear that in this case ˆα T (m) isan odd function of ε, and depends on σ 1, σ 2 and µ 1 individually. i= Forecast Error Decomposition Using (20) and (9) in (8), and recalling that α 2 = µ 2 1 τ 0 p β 2, then after some algebra the forecast error, e T +1 (m), can be decomposed as e T +1 (m) =σ 2 ε T +1 X 1T (m) X 2T (m) X 3T (m), (24) where µ τ 0 X 1T (m) = v G 0 c µ v 2 px µ τ 0 v G i c µ v 2 ˆβ it (m), (25) i=1 12

14 and X 2T (m) = τ 0 vg 0 Hε v px µ τ 0 v G i Hε ˆβ v it (m), (26) i=1 X 3T (m) =(x T µ 2 τ p ) 0 ³ˆβ T (m) β 2. (27) The first term in this decomposition refers to future uncertainty which is independently distributed of the other terms. The second term, X 1T (m), is due to the mean shift and disappears under µ 1 = µ 2 = µ. Recall that in this case v 1 τ 0 vg i c =µ, for all i. 4 The third term, X 2T (m), captures the uncertainty associated with the unconditional mean of the process and reduces to zero if µ 1 = µ 2 =0. Thelast term represents the slope uncertainty and depends on whether the analysis is carried out unconditionally, or conditionally on x T =(y T,y T 1,..., y T p+1 ) 0,inwhich case the extent of the bias will generally depend on the size of the gap x T µ 2 τ p Unconditional MSFE To obtain the unconditional form of e T +1 (m), we first note that x T can be written as S p y T (m), where S p =(0 p (v p).j p ), and J p is the p p matrix Therefore, using (19) we have J p = x T µ 2 τ p =(S p G 0 c µ 2 τ p )+S p G 0 Hε, and X 3T (m), defined by (27), decomposes further as X 3T (m) =(S p G 0 c µ 2 τ p ) 0 ³ˆβT (m) β 2 +(S p G 0 Hε) 0 ³ˆβT (m) β 2. 4 See the last section of Appendix B. Note also that X 1T (m) does not disappear if β 2 = τ 0 pβ 2 = 1, solongasµ 1 6= µ 2. However, under β 2 = 1, itsimplifies to ³ X 1T (m) =(µ 1 µ 2 ) 1 τ 0 pˆβ T (m). 13

15 However, under µ 1 = µ 2 = µ the first term of X 3T (m) vanishes and we have 5 e T +1 (m) =σ 2 ε T +1 X 2T (m) ³ˆβT (m) β 2 0 Sp G 0 Hε. (28) Also under µ 1 = µ 2 = µ, e T +1 (m), and hence E ε e 2 T +1 (m), do not depend on the unconditional mean of the autoregressive process. The computation of E ε e 2 T +1 (m) can be carried out via stochastic simulations. We have Ê R e 2 T +1 (m) = σ R RX r=1 h X (r) 1T (m)+x(r) 2T (m)+x(r) 3T (m) i 2, where the terms X (r) it (m),i =1, 2, 3 can be computed using random draws from ε N(0, I ν+p ), whichwedenotebyε (r), r =1, 2,..., R. In particular, µ X (r) τ 0 1T (m) = v G 0 c µ v 2 X (r) 0 2T (m) =τ vg 0 Hε (r) v px µ τ 0 v G i c µ v 2 i=1 Ã! px τ 0 vg i Hε (r) i=1 X (r) 3T (m) =(S pg 0 c µ 2 τ p ) 0 ³ˆβ(r) T (m) β 2 + ˆβ (r) it (m), (29) ˆβ (r) it (m), (30) v ³ S p G 0 Hε (r) 0 ³ˆβ(r) T (m) β 2, (31) ˆβ (r) it (m) denotestheestimateofβ i based on ε (r). Assuming E ε e 2 T +1 (m) and exists, then due to the independence of ε (r) across r, andthefactthatx (r) it also independently and identically distributed across r, we have (as R ) Ê R e 2 T +1 (m) p Eε e 2 T +1 (m). (m) are The following proposition generalizes Theorem in Fuller (1996, page 445) to the case where estimation has been based on an AR(p) model which has been subject to breaks in the slope coefficients and/or error variances. Proposition 2: The one-step ahead forecast errors, e T +1 (m), defined by (8) from the AR(p) model, (2), subject to a break in the AR coefficients (β 1 6= β 2 )or a break in the innovation variance (σ 2 1 6= σ 2 2) are unbiased provided that: (i) The probability distribution of ε =(ε 0, ε T +1 ) 0 is symmetrically distributed around E(ε )=0, and its first and second order moments exist; 5 Note that in this case S p G 0 c =µs p τ v = µτ p. 14

16 (ii) The first-order moments of the estimated slope coefficients, ˆβ it (m), exist, namely E ˆβ it (m) <, for i =1, 2,..., p; (iii) There is no break in the mean of the process, µ 1 = µ 2. as Proof: Under µ 1 = µ 2, using (26) and (28), the prediction error can be written e T +1 (m) = σ 2 ε T +1 ³ˆβT (m) β 2 0 Sp G 0 Hε " τ 0 v G 0Hε px µ # τ 0 v G i Hε ˆβ v v it (m). It is clear that under assumption (i) the terms σ 2 ε T +1, β 0 2S p G 0 Hε, andτ 0 vg 0 Hε, which are linear functions of ε, have mean zero and we have i=1 i E ε [e T +1 (m)] = E ε hˆβ 0 T (m)s p G 0 Hε + px i=1 µ τ 0 E v G i Hε ε ˆβ v it (m). Also under µ 1 = µ 2 and by Proposition 1, ˆβ T (m), is an even function of ε. Hence, ˆβ 0 T (m)s pg 0 Hε, and(τ 0 vg i Hε) ˆβ it (m) fori =1, 2,..., p are odd functions of ε, and under assumptions (i) and (ii) their expectations exist and are equal to zero by the symmetry assumption. Therefore, E ε [e T +1 (m)] = 0. Inthecasewhereµ 1 6= µ 2, ˆβ jt (m) is not an even function of ε, the term X 1T defined by (25) does not vanish and the prediction error given by (24), is no longer anoddfunctionofε, so it will, in general, not have a zero mean. Remark: Conditions under which moments of ˆβ it (m) exists in the case of AR(1) models with fixed coefficients have been investigated in the literature and readily extends to AR(1) models subject to breaks. For the AR(1) model under µ 1 = µ 2 we have [see (21)] ˆβ T (m) = ε0 H 0 G 0 1M τ G 0 Hε ε 0 H 0 G 0 1M τ G 1 Hε. Assuming that ε is normally distributed and applying a Lemma due to Smith (1988) to (ε 0 H 0 G 0 1M τ G 1 Hε) 1, it is easily established that the r th moment of 15

17 ˆβ T (m) existsifrank (H 0 G 0 1M τ G 1 H)=v 1=T m>2r. 6 Hence, ˆβ T (m) hasa first-order moment if T>m+ 2. To our knowledge no such conditions are known for higher order AR processes, even with fixed coefficients. Proposition 2 has important implications for the trade-off that exists in the estimation bias of the slope and intercept coefficients in the AR models even in the presence of breaks so long as µ 1 = µ 2 = µ. To see this notice from (22) that i E [ˆα T (m) α 2 ]= µ E hˆβ T (m) β 2. This provides an interesting relationship between the small sample bias of the estimator of the intercept term, E [ˆα i T (m) α 2 ], and the small sample bias of the long-run coefficient, E hˆβ T (m) β 2. The estimator of the intercept term, ˆα T (m), is unbiased only if the sample mean is zero. But, in general there is a spill-over effect from the bias of the slope coefficienttothatoftheinterceptterm. For the AR(1) model the results simplify further and we have i E [ˆα T (m) α 2 ]= µ E hˆβt (m) β 2. (32) i Since E hˆβt (m) β 2 < 0, it therefore follows that E [ˆα T (m) α 2 ] > 0ifµ>0, E [ˆα T (m) α 2 ] 0ifµ 0. Once again these results hold irrespective of whether β 1 = β 2 or not Conditional MSFE As before we have e T +1 (m) =σ 2 ε T +1 X 1T (m) X 2T (m) X 3T (m), where X it (m), i = 1, 2, 3, are defined by (25), (26), and (27). In computing the conditional MSFE, defined by E ε e 2 T +1 (m) Ω T,wefix xt and integrate with respect to the distribution of ε. Recall that ˆβ T (m) andˆα T (m) asdefinedin(6)and (7) are only functions of ε and are hence not constrained by the terminal value, 6 Note that H is full rank, rank(g i )=v, andrank(m τ )=v 1. 16

18 x T. 7 To investigate the effect of parameter estimation uncertainty we therefore draw values of ε independently of x T. Once again the results simplify when µ 1 = µ 2 = µ. In this case X 1T (m) = 0, X 2T (m) is an odd function of ε, and assuming that the distribution of ε is symmetric we have E ε [e T +1 (m) Ω T ]= (x T µτ p ) 0 E ε ³ˆβT (m) β 2. Suppose p =1, so that it is easy to characterize when x T is above or below the mean. Then E ε [e T +1 (m) Ω T ]= (y T µ) E ε ³ˆβT (m) β 2. (33) Since, E ε ³ˆβT (m) β 2 < 0, E ε [e T +1 (m) Ω T ]= ( > 0 if y T >µ 0 if y T µ, (34) and the estimated model under-predicts if the last observation is above the unconditional mean (y T >µ), while conversely it over-predicts if the last observation is below the unconditional mean (y T <µ). Therefore, conditional predictions tend to be biased towards the unconditional mean of the process. As with the unconditional MSFE, the computation of the conditional MSFE can also be carried out by stochastic simulations. In general, for a given value of x T, and using draws from ε N(0, I ν+p )wehave Ê R e 2 T +1 (m) Ω T = σ R RX h i 2 X (r) 1T (m)+x(r) (r) 2T (m)+ X 3T (m), (35) r=1 where X (r) 1T term, X(r) 3T (m) andx(r) 2T (m) are given by (29) and (30), as before, with the third (m), now defined by X (r) 3T (m) =(x 0 (r) T µ 2 τ p ) ³ˆβ T (m) β 2. (36) Once again as R, we would expect ÊR e 2 T +1 (m) Ω T p Eε e 2 T +1 (m) Ω T. 7 This is consistent with the approach taken in calculating asymptotic results, c.f. Fuller (1996). If we literally condition on the full path of y-values in Ω T,thenˆβ T (m) andˆα T (m) areofcourse non-random (fixed) constants and no estimation uncertainty arises. 17

19 4. Numerical Results Our approach is quite general and allows us to study the small sample properties of AR models in some detail. The existing literature has focused on the AR(1) model without a break, where the key parameters affecting the properties of the OLS estimators, ˆα T (m) andˆβ T (m), are the sample size and the persistence parameter, β 1. In our setting there are many more parameters to consider. In the absence of a break there are now p autoregressive parameters plus the intercept, α, and the innovation variance, σ 2. Under a single break, we need to consider both the pre- and post-break parameters - i.e. the AR coefficients (β 1, β 2 ), the intercepts (α 1, α 2 ) and the innovation variances (σ 2 1, σ 2 2). Furthermore, how the total sample divides into pre- and post-break periods (v 1 and v 2 ) is now crucial to the bias in the post-break parameter estimates and to the bias and variance of the forecast error. To ensure that our results are comparable to the existing literature, our benchmark model is the AR(1) specification without a break (experiment 1 in Table 1). We study breaks in the autoregressive parameter in the form of both moderately sized (0.3) and large (0.6) breaks in either direction (experiments 2-4) as well as a unit root process in the post-break (experiment 5) or pre-break (experiment 9) period. We also consider pure breaks in the innovation variance (experiments 6 and 7), where σ changes between values of 1/4 and 1 or 4 and 1, and in the mean (experiment 8), where µ changes between 1 and 2. For convenience the parameter values assumed in each of the experiments are summarized in Table 1. Since our focusisontheeffect of breaks on the bias and forecasting performance of AR models, results are presented as a function of the pre-break window size (v 1 )and the post-break window size (v 2 ). We vary v 1 from zero (no pre-break information) through 1, 2, 3, 4, 5, 10, 20, 30, 50 and 100, while the post-break window, v 2,is set at 10, 20, 30, 50 and 100. Simulation results are presented in Tables 2-5. Results are based on 50,000 Monte Carlo simulations with innovations drawn from an IID Gaussian distribution. 8 Table 2 shows the bias in ˆβ 1 while Table 3 shows the conditional bias in the forecast for a situation where y T is above its mean, i.e., y T = µ 2 + σ 2. 9 To measure 8 We also considered an AR(2) specification to study the effect of higher order dynamics. Results were very similar to those reported below and are available from the authors web site. 9 Estimated values are computed as averages across Monte Carlo simulations relative to the 18

20 forecasting performance, Table 4 reports the unconditional RMSFE while Table 5 shows the RMSFE conditional on y T = µ 2 + σ 2, as functions of the pre-break (v 1 ) and post-break (v 2 ) window sizes. We condition on this particular value since if y T = µ 2 the conditional bias is zero while if y T = µ 2 σ 2 the conditional bias takes the same value but with the sign reversed, c.f. (33) Bias Results First consider the bias in ˆβ 1. In the absence of a break, ˆβ 1 is downward biased with a bias that disappears as v 1 and v 2 increase and becomes quite small when the combined sample v = v 1 + v 2 is sufficiently large. 10 Notice the symmetry of the results in v 1 and v 2 which follows since (under no break) only v 1 + v 2 matters for the bias. 11 Once a break is introduced in the AR parameter, the bias in ˆβ 1 continues to decline in v 2 but need no longer decline monotonically as a function of v 1. The reason for this is simple: including pre-break data generated by a different (less persistent) process introduces a new bias term in ˆβ 1. It is only to the extent that this term is offset by a reduction in the small sample bias of the AR estimate that inclusion of pre-break data will lead to a bias reduction. Thus, when v 2 is very large (e.g., 50 or 100 post-break observations) the small sample bias in ˆβ 1 based purely on post-break observations is already quite small. In this situation, inclusion of pre-break data will not lower the bias in ˆβ 1. Conversely, when the post-break sample is small (i.e., v 2 =10 20 observations), the small sample bias in ˆβ 1 is very large and including up to 30 pre-break observations will actually reduce the bias under a moderately sized break. Naturally, if the break size is large (experiment 4), this effect is reduced since the true bias due to including pre-break observations in the estimation window dominates any reduction in the small sample bias in ˆβ 1 true post-break values. To ensure comparability across the experiments they are based on the same random numbers. 10 The bias estimates are in line with the well known Kendall (1954) approximation formula ³ˆβ1 E β 1 = (1 +3β 1) + O(v 3/2 ),v= v 1 + v 2. v 11 Recall from (32) that in the case of Gaussian errors the bias in ˆα T (m) can be exactly inferred from the bias of ˆβ T (m) when there is no break in the mean. For this reason we focus our analysis on the bias in ˆβ T (m). 19

21 based solely on post-break data for all but the smallest post-break window sizes. Interestingly, when the break is in the reverse direction (experiment 3) so that the true value of β 1 declines, including a small number of pre-break data points leads to a reduction in the bias in ˆβ 1 even for very large post-break windows. For example, the bias in ˆβ 1 is minimized by including 3 pre-break observations even when v 2 = 100. The reason is again related to the direction of the small sample bias in ˆβ 1.Sinceˆβ 1 is downward biased, when the break is from high to low persistence, the (upward) bias introduced by inclusion of the more persistent pre-break data works in the opposite direction of the small sample (downward) bias in ˆβ 1.Forthis reason the biases under a decline in β 1 tend to be smaller than the biases observed when β 1 increases at the time of the break. Under a post-break unit root (experiment 5) the bias-minimizing pre-break window size is around 20 observations. Under a pre-break unit-root (experiment 9), bias is smallest for either v 1 =0orv 1 = 1. When a break occurs in the innovation variance (experiments 6 and 7), the smallest bias is always achieved by the longest pre-break windows. The only difference to the case without a break is that the bias is no longer a symmetric function of v 1 and v 2. Allowing for a break in the mean (experiment 8), the forecast error is no longer unbiased unconditionally and the optimal pre-break window size rises to 100 irrespective of the value of v 2. Turning next to the conditional bias in the forecast, Table 3 shows that, in the absence of a break, the bias is positive when the prediction is made conditional on y T = µ 2 + σ 2, a value above the mean of the process. This is, of course, consistent with (34) and with the sign of the bias in ˆβ 1. In general, the results for the conditional bias in the forecast error mirror those of the bias in ˆβ 1, except for the case with a break in the mean. Whereas the bias in ˆβ 1 was reduced the larger the value of v 1 when the mean increases at the time of the break, the bias in the forecast error is smallest when v 1 = 0 and the mean increases assuming a large post-break sample (v 2 = 50 or 100) Forecasting Performance To measure forecasting performance for the AR(1) model, unconditional and conditional RMSFE values are shown in Tables 4 and 5. Under no break the unconditional RMSFE is 1.15 for the smallest combined sample (v 1 =0,v 2 = 10) and it declines symmetrically as a function of v 1 and v 2. In the presence of a moderate 20

22 break in the AR coefficient, the unconditional RMSFE continues to decline as a function of v 2 but it no longer declines monotonically in v 1, the pre-break window. Furthermore, the unconditional RMSFE no longer converges to one - its theoretical value in the absence of parameter estimation uncertainty - provided the ratio v 1 /v 2 does not go to zero. For example, when v 1 = v 2 = 100, the unconditional RMSFE under a moderate break in β 1 is close to 1.02 as opposed to a value of observed in the case without a break. This difference is due to the squared bias in the AR parameters introduced by including pre-break data points. Generally, the windows that minimize the unconditional RMSFE tend to be longer than the windows that minimize the bias. Increasing the window size beyond the point that produces the smallest bias may be acceptable if it reduces the forecast error variance by more than the associated increase in the squared bias. A moderately sized break in β 1 implies that the optimal pre-break window size declines to observations under the unconditional RMSFE criterion although it remains much longer under the conditional RMSFE criterion. In both cases, the optimal value of v 1 is smaller, the larger the value of v 2 and the larger the size of the break in β 1 as can be seen by comparing the results from experiments 2 and 4. Somewhat different patterns emerge when the AR model switches from having a unit root process to being stationary and vice versa. Under a post-break unit root the conditional RMSFE is minimized for rather large values of v 1,whereas the unconditional RMSFE is minimized at much smaller values of v 1, typically below 10 observations. But, under the pre-break unit root scenario, the smallest unconditional and conditional RMSFE values are produced by at most including one or two pre-break observations. When the post-break innovation variance is higher, it is optimal to set the prebreak window as long as possible since this maximizes the length of the less noisy data and thus brings down the forecast error variance without introducing a bias in the forecast. In contrast, when the innovation variance declines at the time of the break, the optimal pre-break window size is only long provided the post-break window, v 2,israthershortanditdeclinestozeroforlargervaluesofv 2. Notice how the performance of the forecast can deteriorate badly upon the inclusion of a single pre-break data point even with quite long post-break windows. This is due to the extra noise introduced by using pre-break data for parameter estimation. Under a break to the mean (experiment 8), the lowest conditional and uncon- 21

23 ditional RMSFE values are observed for the longer pre-break windows. This is an interesting finding and holds despite the fact that additional bias is introduced into the forecast. For example, in Table 4 the RMSFE is systematically reduced by increasing the pre-break window, v 1. In practice, breaks are likely to involve the meansaswellastheslopecoefficients. In such situations our results suggest that, at least for breaks of similar size to those assumed here, it is difficult to outperform the forecasting performance generated by a model based on an expanding window of the data Forecasting Performance of Rolling, Expanding and Post-break windows To shed light on the practical implications of our results, we next consider the outof-sample forecasting performance of a range of widely used estimation windows. One way to deal with parameter instability is to use a rolling observation window. The size of the rolling window is often decided by aprioriconsiderations. Here we consider a short rolling window using the most recent 25 observations and a relatively long rolling window based on the most recent 50 observations. If parameter instability is believed to be due to the presence of rare structural breaks, another possibility is to only use post-break data. In some cases the timing of the break may be known, but in most cases both the timing and the number of breaks must be estimated. We therefore use the Bai-Perron (1998) method to test for the presence of structural breaks and determine their timing, allowing up to three breaks and selecting the number of breaks by the Schwarz information criterion. If one or more breaks is identified at time t, this procedure uses data after the most recent break date to produce a forecast for period t+1. Ifnobreakisidentified, an expanding data window is used to generate the forecast. Finally, as a third option an expanding window is considered. This is the most efficient estimation method in the absence of breaks and provides a natural benchmark. We initially undertook the following simulation exercise. For each of the original AR(1) experiments we assume a break has taken place at observation 101. Our post-break forecast evaluation period runs from observations 111 to 150. For this period we computed RMSFEs of the one-step ahead forecasts obtained under different estimation windows by Monte Carlo simulation. Panel A of Table 6 reports the results under a single break. As expected, when a break is not present the expanding window method produces the lowest RMSFE 22

24 values. The expanding window also performs well when the break only affects the volatility or the mean parameter. The fact that the expanding window performs best even when the pre-break volatility is higher than the post-break volatility can be explained by the reduction in the variance of the parameter estimation error due to using a very long estimation window. The finding for a break in the mean is consistent with the simulation results in Table 4. In the experiments with a very large change in the autoregressive parameter (experiments 4-5), the short rolling window method produces the best performance, while the long rolling window works best for smaller breaks (experiments 2-3) which generate a lower squared bias. Interestingly, the use of a post-break window with an estimated break point does not produce the lowest RMSFE performance in any of the experiments 1-8. A possible explanation of this finding lies in the modest power of break point tests to detect changes in autoregressive parameters as documented by Banerjee, Lumsdaine and Stock (1992). The only case where the post-break window method results in the lowest RMSFE is under a pre-break unit root (experiment 9). For this case the expanding window method performs quite poorly. This is consistent with our simulation results which showed that the conditional and unconditional RMSFE performance was best for very small - frequently zero - pre-break windows under a pre-break unit root. We also modified the simulation with the pre-break unit root to ensure that the point towards which the post-break process mean reverts is the terminal point of the pre-break unit root process (experiment 10) rather than simply µ 2. This is likely to generate sample paths more similar to those observed in practice, c.f. Banerjee, Lumsdaine and Stock (1992). The results show that although the expanding window method performs relatively better, it still does not produce the lowest RMSFE Multiple Breaks So far we have focused on the case with a single structural break, but in practice the time series process under consideration may be subject to multiple breaks. Our procedure can readily be generalized to account for this possibility. Accordingly, we extended our simulation experiments to allow for two breaks occurring after 50 and 100 observations, respectively. The presence of multiple breaks raises questions concerning the process generating the breaks. Barring a general theory we consider 23

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